Discrete-time signal processing
Discrete-time signal processing
Elements of information theory
Elements of information theory
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Independent Component Analysis: Principles and Practice
Independent Component Analysis: Principles and Practice
Performance comparison of subspace rotation and MUSIC methods fordirection estimation
IEEE Transactions on Signal Processing
A maximum likelihood approach to blind multiuser interferencecancellation
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Joint timing recovery and decoding algorithms for non-binary LDPC coded systems
Digital Signal Processing
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Many important problems in signal processing can be reduced to the adequate selection of the parameters of a (possibly nonlinear) filter in order to obtain an output signal that complies with some desired properties. In this work, we analyze a novel criterion for selecting filter parameters that relies on the ability to characterize the desired filter output in terms of a target probability density function (pdf). This target pdf can be handled as a likelihood function to be maximized, thus we refer to the new criterion as maximum target-likelihood (MTL). We present a very general signal model where the MTL criterion can be applied and derive necessary and sufficient conditions for asymptotic convergence of the method. The relationship and differences between MTL and standard maximum likelihood (ML), minimum Kullback-Leibler divergence (MKLD), and minimum entropy (ME) methods are explored. Finally, as an example, we apply the novel criterion to the problem of blind timing and phase recovery in a digital transmission system and show that the resulting algorithm is competitive with existing non-data-aided ML-based algorithms.